In CT systems, an x-ray source projects a fan-shaped beam which is collimated to lie within an X-Y plane of a Cartesian coordinate system, termed the "imaging plane". The x-ray beam passes through the object being imaged, such as a patient, and impinges upon a linear array of radiation detectors. The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object. Each detector of the linear array produces a separate electrical signal that is a measurement of the beam attenuation. The attenuation measurements from all the detectors are acquired separately to produce a transmission profile.
The x-ray source and the linear detector array in a CT system are rotated with a gantry within the imaging plane and around the object so that the angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements from the detector array at one gantry angle is referred to as a "view". A "scan" of the object comprises a set of views made at different gantry angles during one revolution of the x-ray source and detector. In an axial scan, data is processed to construct an image that corresponds to a two dimensional slice taken through the object. One method for reconstructing an image from a set of data is referred to in the art as the filtered back projection technique. This process converts the attenuation measurements from a scan into integers called "CT numbers" or "Hounsfield units", which are used to control the brightness of a corresponding pixel on a cathode ray tube display.
Detector arrays are constructed from a plurality of detectors cells. The cells may deteriorate to the extent that artifacts are introduced to the images. Visually, these artifacts may appear as rings or bands in an image. Particularly, the lack of uniformity across the detector array and the lack of similarity among the degraded cells causes artifacts in the shape of rings or bands when a sloped object is scanned. It is desirable, of course, to minimize artifacts in an image.
Known algorithms exist for removing the rings from an image. Particularly, the rings may be detected and corrected in the projection space or in the image space. For example, in a known projection space correction algorithm, an "error candidate vector" is generated based on the filtration of pre-processed projections. The vector is then compared to the vectors generated from previous views to determine the amount of the correction required. Such an algorithm is described in U.S. Pat. No. 5,301,108, which is assigned to the present assignee.
With another known ringfix algorithm, a ring is identified by performing a high pass radial filtering. In the actual implementation, the radial filter is approximated by the weighted sum of horizontal and vertical one dimensional filters. Based on the filtering result and predefined criteria, errors in each previously defined segment in the image is azimuthally combined to determine the ring error for the entire segment. Ring artifacts are removed by subtracting the error from the original image on a segment by segment basis.
With known algorithms, the filter length cannot be adapted as a function of pixel size. Particularly, the kernel size and shape of the high pass filter is kept constant. This is non-optimal since the frequency response of the high pass filter depends strongly on the image pixel size. To maintain a relatively constant frequency response of the filter over the entire display field of view (DFOV), the kernel size of the boxcar filter needs to vary as a function of the DFOV.
Another shortcoming of known algorithms is that such algorithms cannot be adapted to the error patterns. The width of a ring generated by an isolated channel will be roughly half as wide as the one generated by two adjacent channels. Therefore, in order to ensure that the filter can detect and correct double cell rings as effectively as the single cell rings, the filter length has to be adjusted accordingly. Naturally, this requires a-priori knowledge of the detector characteristics.
Yet another shortcoming of known algorithms is their inability to deal with rings of high magnitude. Whenever a deep ring is present in the image, the known ringfix algorithms will correct it only to the extent of reducing its magnitude. After the ringfix correction, the residual ring is still quite visible. For many rings associated with degraded detectors, the magnitude of the rings are fairly deep and therefore cannot be corrected by the existing ringfix algorithms.